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Metrics & Events

Toggly tracks metrics and events to help you understand feature usage, measure business impact, and detect anomalies.

Types of Metrics​

Feature Usage Metrics​

Toggly tracks three types of feature interactions to help you understand the full user journey:

  • Checks: How many times a feature was evaluated (automatic)
  • Views: How many users saw the feature UI (manual)
  • Usage: How many users interacted with the feature (manual)

These metrics let you calculate eligibility rates, view rates, and conversion rates for your features.

Learn More

See Feature Usage Tracking for a detailed guide on checks, views, and usage metrics.

Business Metrics​

Custom metrics you define to measure business impact:

  • Conversion Rate: Percentage of users who convert
  • Revenue: Revenue generated
  • Engagement: User engagement metrics
  • Retention: User retention rates

System Performance Metrics​

Track the health and performance of your application infrastructure:

  • Resource Usage: CPU load, memory consumption, thread counts
  • Stability: Exception rates, error counts, crash reports
  • Throughput: Request rates, garbage collection events

These metrics help you correlate feature releases with system stability (e.g., "Did enabling the new video processor spike our CPU usage?").

Metrics Dashboard​

The Toggly dashboard provides:

  • Real-time Metrics: Live updates of feature usage
  • Historical Trends: Charts showing metrics over time
  • Comparison Views: Compare metrics across features or experiments
  • Anomaly Detection: Automatic alerts for unusual patterns

Anomaly Detection​

Toggly automatically detects anomalies in your metrics:

  • Sudden Spikes: Unusual increases in feature usage
  • Drops: Unexpected decreases in metrics
  • Pattern Changes: Shifts in user behavior
  • Error Rate Increases: Spikes in error rates

When anomalies are detected, you'll receive:

  • Dashboard Alerts: Visual indicators in the dashboard
  • Email Notifications: Optional email alerts
  • Webhook Notifications: Real-time webhook calls

Metric Aggregation​

Metrics are aggregated at multiple levels:

  • Per Feature: Metrics for individual features
  • Per Experiment: Metrics for experiments
  • Per Environment: Metrics per environment
  • Overall: Aggregate metrics across all features

Next Steps​